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@Article{alshamrani_reverse_2007,
  Title                    = {Reverse logistics: simultaneous design of delivery routes and returns strategies},
  Author                   = {Ahmad Alshamrani and Kamlesh Mathur and Ronald H. Ballou},
  Journal                  = {Computers \& Operations Research},
  Year                     = {2007},

  Month                    = feb,
  Number                   = {2},
  Pages                    = {595--619},
  Volume                   = {34},

  __markedentry            = {[Andres Jaque]},
  Abstract                 = {A reverse logistics problem, motivated by blood distribution of the American Red Cross, is examined where containers in which products are delivered from a central processing point to customers (stops) in one period are available for return to the central point in the following period. Any container not picked up in the period following its delivery incurs a penalty cost resulting primarily from operating costs and customer dissatisfaction. The result is a dynamic logistics planning problem where in each delivery period the vehicle dispatcher needs to design a multi-stop vehicle route while determining the container quantities to be picked up at each stop. This research is unique in that route design and pickup strategies are developed simultaneously, where stop volumes are known only probabilistically over a planning horizon. A heuristic procedure is developed for treating the route design-pickup strategy planning problem. Keywords: Reverse logistics; Vehicle routing; Pickup and delivery },
  Doi                      = {10.1016/j.cor.2005.03.015},
  Keywords                 = {Pickup and delivery, Reverse logistics, Vehicle routing},
  Owner                    = {Andres Jaque},
  Review                   = {En este trabajo se trata un problema de din{\'a}mico de planeaci{\'o}n de log{\'i}stica reversa motivado por la distribuci{\'o}n de sangre del American Red Cross, en este problema los veh{\'i}culos transportan contenedores con el producto desde un dep{\'o}sito central a diferentes clientes (paradas), para luego retornar al dep{\'o}sito recogiendo otros contenedores, evidenciando un problema din{\'a}mico en el que en cada periodo de entrega el veh{\'i}culo despachador necesita dise{\~n}ar una ruta de m{\'u}ltiples clientes, mientras determina las cantidades a ser recogidas en cada cliente, en este problema los vol{\'u}menes de clientes son estoc{\'a}sticos y se conocen probabil{\'i}sticamente sobre un horizonte de planeaci{\'o}n. Para solucionar este problema se propone un algoritmo heur{\'i}stico Or-opt, sin embargo se observo que la aplicaci{\'o}n simple del problema tiene un costo computacional muy alto, por lo que se proponen diferentes reglas y estrategias heur{\'i}sticas para hacer el algoritmo computacionalmente viable, sin degradar la calidad de las soluciones. El problema tratado es novedoso y no ha sido tratado suficientemente con anterioridad, es complejo, din{\'a}mico, y presenta un interesante desaf{\'i}o para las soluciones propuestas.},
  Timestamp                = {2009.05.03},
  Url                      = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC5-4G0B751-3&_user=10&_coverDate=02%2F28%2F2007&_alid=803998645&_rdoc=142&_fmt=high&_orig=search&_cdi=5945&_st=13&_docanchor=&view=c&_ct=543&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=317b4bda2c7a58691fbae5c203270e6c}
}

@Book{Bellman_dynamicprog_1957,
  Title                    = {Dynamic Programming},
  Author                   = {Richard Bellman},
  Publisher                = {Princeton University Press},
  Year                     = {1957},

  Owner                    = {andres},
  Timestamp                = {2010.11.14}
}

@Article{bellman_theory_1954,
  Title                    = {The theory of dynamic programming},
  Author                   = {R. Bellman},
  Journal                  = {Bull. Amer. Math. Soc},
  Year                     = {1954},
  Number                   = {6},
  Pages                    = {503--515},
  Volume                   = {60}
}

@Article{Bertsekas,
  Title                    = {Dynamic programming and Stochastic Control},
  Author                   = {Dimitri Bertsekas},
  Journal                  = {Journal of the American Statical association},
  Year                     = {1979},
  Pages                    = {510--511},
  Volume                   = {74},

  Owner                    = {emily},
  Timestamp                = {2009.10.18}
}

@InProceedings{Bertsekas1996,
  Title                    = {Neuro-dynamic programming},
  Author                   = {Bertsekas, D.P. and Tsitsiklis, J.N.},
  Booktitle                = {Proceedings of 1995 34th IEEE Conference on Decision and Control},
  Pages                    = {560--564},
  Publisher                = {IEEE},
  Volume                   = {1},

  Doi                      = {10.1109/CDC.1995.478953},
  ISBN                     = {0-7803-2685-7},
  ISSN                     = {0191-2216},
  Keywords                 = {Artificial intelligence,Artificial neural networks,Control systems,Cost function,Dynamic programming,Equations,Laboratories,Optimal control,State-space methods,Uncertainty,approximation theory,artificial intelligence,cognitive science,cognitive systems,dynamic programming,neural nets,neural networks,neuro-dynamic programming,simulation,uncertainty,uncertainty handling},
  Language                 = {English},
  Mendeley-tags            = {dynamic programming},
  Owner                    = {ajaque},
  Timestamp                = {2015.01.29},
  Url                      = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=478953}
}

@Book{bertsekas_dynamic_2007,
  Title                    = {Dynamic Programming and Optimal Control, Vol. {II}},
  Author                   = {Dimitri P. Bertsekas},
  Publisher                = {Athena Scientific},
  Year                     = {2007},

  Abstract                 = {A major revision of the second volume of a textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The second volume is oriented towards mathematical analysis and computation, and treats infinite horizon problems extensively. New features of the 3rd edition are: 1) A major enlargement in size and scope: the length has increased by more than 50\%, and most of the old material has been restructured and/or revised. 2) Extensive coverage (more than 100 pages) of recent research on simulation-based approximate dynamic programming (neuro-dynamic programming), which allow the practical application of dynamic programming to large and complex problems. 3) An in-depth development of the average cost problem (more than 100 pages), including a full analysis of multichain problems, and an extensive analysis of infinite-spaces problems. 4) An introduction to infinite state space stochastic shortest path problems. 5) Expansion of the theory and use of contraction mappings in infinite state space problems and in neuro-dynamic programming. 6) A substantive appendix on the mathematical measure-theoretic issues that must be addressed for a rigorous theory of stochastic dynamic programming. Much supplementary material can be found in the book's web page: http://www.athenasc.com/dpbook.html},
  ISBN                     = {1886529302, 9781886529304},
  Url                      = {http://portal.acm.org/citation.cfm?id=1396348}
}

@Book{Bertsekas1997,
  Title                    = {Differential Training Of Rollout Policies},
  Author                   = {Dimitri P. Bertsekas},
  Year                     = {1997},

  Abstract                 = {We consider the approximate solution of stochastic optimal control problems using a neurodynamic programming/reinforcement learning methodology. We focus on the computation of a rollout policy, which is obtained by a single policy iteration starting from some known base policy and using some form of exact or approximate policy improvement. We indicate that, in a stochastic environment, the popular methods Q-factor and cost-to-go values. In particular, we propose a method, called differential training, that can be used to obtain an approximation to cost-to-go differences rather than cost-to-go values by using standard methods such as TD(\#) and \#-policy iteration. This method is suitable for recursively generating rollout policies in the context of simulation-based policy iteration methods.},
  File                     = {:home/andres/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/CZTNTIAG/Bertsekas - 1997 - Differential Training Of Rollout Policies.pdf:pdf},
  Url                      = {http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=7B1AFE58D388186844400369B0F2B24B?doi=10.1.1.46.5702&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.5702}
}

@Book{bertsekas_dynamic_1995,
  Title                    = {Dynamic Programming and Optimal Control},
  Author                   = {Dimitri P. Bertsekas},
  Publisher                = {Athena Scientific},
  Year                     = {1995},

  ISBN                     = {9781886529113},
  Keywords                 = {Control theory, dynamic programming}
}

@Article{bertsimas_vehicle_1992,
  Title                    = {A Vehicle Routing Problem with Stochastic Demand},
  Author                   = {Dimitris J. Bertsimas},
  Journal                  = {Operations Research},
  Year                     = {1992},

  Month                    = may,
  Number                   = {3},
  Pages                    = {574--585},
  Volume                   = {40},

  Abstract                 = {We consider a natural probabilistic variation of the classical vehicle routing problem {(VRP)}, in which demands are stochastic. Given only a probabilistic description of the demand we need to design routes for the {VRP.} Motivated by applications in strategic planning and distribution systems, rather than resolving the problem when the demand becomes known, we propose to construct an a priori sequence among all customers of minimal expected total length. We analyze the problem using a variety of theoretical approaches. We find closed-form expressions and algorithms to compute the expected length of an a priori sequence under general probabilistic assumptions. Based on these expressions we find upper and lower bounds for the probabilistic {VRP} and the {VRP} re-optimization strategy, in which we find the optimal route at every instance. We propose heuristics and analyze their worst case performance as well as their average behavior using techniques from probabilistic analysis. Our results suggest that our approach is a strong and useful alternative to the strategy of re-optimization in capacitated routing problems.},
  Doi                      = {10.1287/opre.40.3.574},
  File                     = {Snapshot:/home/andres/.mozilla/firefox/q5r35cg1.default/zotero/storage/CXT4CZF7/574.html:text/html},
  ISSN                     = {{0030-364X}, 1526-5463},
  Keywords                 = {networks/graphs: stochastic applications, probability: stochastic model applications, transportation, vehicle routing: stochastic vehicle routing},
  Url                      = {http://or.journal.informs.org/content/40/3/574}
}

@Article{bertsimas_stochastic_1991,
  Title                    = {A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane},
  Author                   = {Dimitris J. Bertsimas and Garrett van Ryzin},
  Journal                  = {Operations Research},
  Year                     = {1991},

  Month                    = aug,
  Note                     = {{ArticleType:} primary\_article / Full publication date: Jul. - Aug., 1991 / Copyright © 1991 {INFORMS}},
  Number                   = {4},
  Pages                    = {601--615},
  Volume                   = {39},

  Abstract                 = {We propose and analyze a generic mathematical model for dynamic, stochastic vehicle routing problems, the dynamic traveling repairman problem {(DTRP).} The model is motivated by applications in which the objective is to minimize the wait for service in a stochastic and dynamically changing environment. This is a departure from classical vehicle routing problems where one seeks to minimize total travel time in a static, deterministic environment. Potential areas of application include repair, inventory, emergency service and scheduling problems. The {DTRP} is defined as follows: Demands for service arrive in time according to a Poisson process, are independent and uniformly distributed in a Euclidean service region, and require an independent and identically distributed amount of on-site service by a vehicle. The problem is to find a policy for routing the service vehicle that minimizes the average time demands spent in the system. We propose and analyze several policies for the {DTRP.} We find a provably optimal policy in light traffic and several policies with system times within a constant factor of the optimal policy in heavy traffic. We also show that the waiting time grows much faster than in traditional queues as the traffic intensity increases, yet the stability condition does not depend on the system geometry.},
  ISSN                     = {{0030364X}},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2065/stable/171167}
}

@Article{bianchi_hybrid_2006,
  Title                    = {Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands},
  Author                   = {Leonora Bianchi and Mauro Birattari and Marco Chiarandini and Max Manfrin and Monaldo Mastrolilli and Luis Paquete and Olivia {Rossi-Doria} and Tommaso Schiavinotto},
  Journal                  = {Journal of Mathematical Modelling and Algorithms},
  Year                     = {2006},

  Month                    = apr,
  Number                   = {1},
  Pages                    = {91--110},
  Volume                   = {5},

  Abstract                 = {Abstract This article analyzes the performance of metaheuristics on the vehicle routing problem with stochastic demands {(VRPSD).} The problem is known to have a computationally demanding objective function, which could turn to be infeasible when large instances are considered. Fast approximations of the objective function are therefore appealing because they would allow for an extended exploration of the search space. We explore the hybridization of the metaheuristic by means of two objective functions which are surrogate measures of the exact solution quality. Particularly helpful for some metaheuristics is the objective function derived from the traveling salesman problem {(TSP)}, a closely related problem. In the light of this observation, we analyze possible extensions of the metaheuristics which take the hybridized solution approach {VRPSD-TSP} even further and report about experimental results on different types of instances. We show that, for the instances tested, two hybridized versions of iterated local search and evolutionary algorithm attain better solutions than state-of-the-art algorithms.},
  Doi                      = {10.1007/s10852-005-9033-y},
  Url                      = {http://dx.doi.org/10.1007/s10852-005-9033-y}
}

@Article{Chepuri,
  Title                    = {Solving the vehicle routing problem with stochastic demands using the cross entropy method},
  Author                   = {Homem-De-Mello T {Chepuri K.}},
  Journal                  = {Annals of Operations Research},
  Year                     = {2005},
  Pages                    = {153--181},
  Volume                   = {55},

  Owner                    = {emily},
  Timestamp                = {2009.10.18}
}

@Article{cheung_dynamic_2008,
  Title                    = {Dynamic routing model and solution methods for fleet management with mobile technologies},
  Author                   = {Bernard {K.-S.} Cheung and {K.L.} Choy and {Chung-Lun} Li and Wenzhong Shi and Jian Tang},
  Journal                  = {International Journal of Production Economics},
  Year                     = {2008},

  Month                    = jun,
  Number                   = {2},
  Pages                    = {694--705},
  Volume                   = {113},

  Abstract                 = {We develop and analyze a mathematical model for dynamic fleet management that captures the characteristics of modern vehicle operations. The model takes into consideration dynamic data such as vehicle locations, travel time, and incoming customer orders. The solution method includes an effective procedure for solving the static problem and an efficient re-optimization procedure for updating the route plan as dynamic information arrives. Computational experiments show that our re-optimization procedure can generate near-optimal solutions.},
  Doi                      = {10.1016/j.ijpe.2007.10.018},
  ISSN                     = {0925-5273},
  Keywords                 = {Dynamic vehicle routing, Heuristics, Mobile technologies},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B6VF8-4RV17J4-1/2/98644318e2f28e442e73c848a2794384}
}

@Article{christiansen_branch-and-price_2007,
  Title                    = {A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands},
  Author                   = {Christian H. Christiansen and Jens Lysgaard},
  Journal                  = {Operations Research Letters},
  Year                     = {2007},

  Month                    = nov,
  Number                   = {6},
  Pages                    = {773--781},
  Volume                   = {35},

  Abstract                 = {This article introduces a new exact algorithm for the capacitated vehicle routing problem with stochastic demands {(CVRPSD).} The {CVRPSD} can be formulated as a set partitioning problem and it is shown that the associated column generation subproblem can be solved using a dynamic programming scheme. Computational experiments show promising results.},
  Doi                      = {10.1016/j.orl.2006.12.009},
  ISSN                     = {0167-6377},
  Keywords                 = {Logistics, Routing, Stochastic programming},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B6V8M-4N3P00V-2/2/726a98831ffd3d9464a139cf9936519a}
}

@InCollection{CrinaGrosan2007,
  Title                    = {Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews},
  Author                   = {{Crina Grosan}, Ajith Abraham},
  Booktitle                = {Hybrid Evolutionary Algorithms},
  Publisher                = {Springer Berlin Heidelberg},
  Year                     = {2007},

  Address                  = {Berlin, Heidelberg},
  Chapter                  = {1},
  Editor                   = {Abraham, Ajith and Grosan, Crina and Ishibuchi, Hisao},
  Pages                    = {pp 1--17},
  Series                   = {Studies in Computational Intelligence},
  Volume                   = {75},

  Abstract                 = {Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.},
  Doi                      = {10.1007/978-3-540-73297-6},
  ISBN                     = {978-3-540-73296-9},
  Keywords                 = {hga},
  Mendeley-tags            = {hga},
  Owner                    = {ajaque},
  Timestamp                = {2015.01.29},
  Url                      = {http://www.springerlink.com/index/10.1007/978-3-540-73297-6}
}

@Booklet{Dantzing1959,
  Title                    = {The Truck Dispatching Problem},
  Author                   = {G. B. Dantzing and J. H. Ramser},
  Month                    = {Oct},
  Year                     = {1959},

  Journal                  = {Management Science},
  Number                   = {1},
  Owner                    = {Andres},
  Pages                    = {80--91},
  Timestamp                = {2009.10.18},
  Volume                   = {6}
}

@InCollection{dessouky_rapid_2006,
  Title                    = {Rapid Distribution of Medical Supplies},
  Author                   = {Maged Dessouky and Fernando Ordonez and Hongzhong Jia and Zhihong Shen},
  Booktitle                = {Patient Flow: Reducing Delay in Healthcare Delivery},
  Year                     = {2006},
  Pages                    = {309--338},

  Abstract                 = {Some important issues in the design of an efficient pharmaceutical supply chain involve deciding where to place the warehouses/inventories and how to route distribution vehicles. Solving appropriate facility location and vehicle routing problems can ensure the design of a logistic network capable of rapid distribution of medical supplies. In particular, both these problems must be solved in coordination to quickly disburse medical supplies in response to a large-scale emergency. In this chapter, we present models to solve facility location and vehicle routing problems in the context of a response to a large-scale emergency. We illustrate the approach on a hypothetical anthrax emergency in Los Angeles County.},
  Owner                    = {Andres},
  Timestamp                = {2009.11.18}
}

@InCollection{Dror_2005,
  Title                    = {Vehicle Routing with Stochastic Demands: Models \& Computational Methods},
  Author                   = {Moshe Dror},
  Booktitle                = {Modeling Uncertainty, International Series in Operations Research \& Management Science},
  Publisher                = {Springer New York},
  Year                     = {2005},

  Owner                    = {andres},
  Timestamp                = {2012.05.01}
}

@Article{Dror1993432,
  Title                    = {Modeling vehicle routing with uncertain demands as a stochastic program: Properties of the corresponding solution},
  Author                   = {Moshe Dror},
  Journal                  = {European Journal of Operational Research},
  Year                     = {1993},
  Number                   = {3},
  Pages                    = {432--441},
  Volume                   = {64},

  Abstract                 = {In this paper we address the issue of different mathematical models for the stochastic vehicle routing problem (SVRP). This problem is inherently much more difficult than the generic deterministic vehicle routing problem (VRP) for which optimal procedures can solve only small problems. Presently, we cannot even begin optimal solution procedures for the SVRP for any problem size exceeding 3 nodes. Thus, we need to examine modeling approaches to this problem in order to exploit the structure and solution properties. We present a multistate stochastic model for the SVRP. We prove that this model has an interesting minimal graph representation in which a SVRP solution corresponds to a Hamiltonian cycle. We also present a Markov decision model for the problem, concluding with a discussion of solution prospects and directions.},
  ISSN                     = {0377-2217},
  Keywords                 = {Stochastic programming},
  Url                      = {http://www.sciencedirect.com/science/article/pii/0377221793901327}
}

@Article{dror_computational_1985,
  Title                    = {A computational comparison of algorithms for the inventory routing problem},
  Author                   = {M. Dror and M. Ball and B. Golden},
  Journal                  = {Annals of Operations Research},
  Year                     = {1985},

  Month                    = dec,
  Number                   = {1},
  Pages                    = {1--23},
  Volume                   = {4},

  Abstract                 = {The inventory routing problem is a distribution problem in which each customer maintains a local inventory of a product such as heating oil and consumes a certain amount of that product each day. Each day a fleet of trucks is dispatched over a set of routes to resupply a subset of the customers. In this paper, we describe and compare algorithms for this problem defined over a short planning period, e.g. one week. These algorithms define the set of customers to be serviced each day and produce routes for a fleet of vehicles to service those customers. Two algorithms are compared in detail, one which first allocates deliveries to days and then solves a vehicle routing problem and a second which treats the multi-day problem as a modified vehicle routing problem. The comparison is based on a set of real data obtained from a propane distribution firm in Pennsylvania. The solutions obtained by both procedures compare quite favorably with those in use by the firm.},
  Doi                      = {{10.1007/BF02022035}},
  Owner                    = {Andres},
  Timestamp                = {2009.11.18},
  Url                      = {http://dx.doi.org/10.1007/BF02022035}
}

@InProceedings{jianhua_fan_multiple_2006,
  Title                    = {A Multiple Vehicles Routing Problem Algorithm with Stochastic Demand},
  Author                   = {Jianhua Fan and Xiufeng Wang and Hongyun Ning},
  Booktitle                = {Intelligent Control and Automation, 2006. {WCICA} 2006. The Sixth World Congress on},
  Year                     = {2006},
  Pages                    = {1688--1692},
  Volume                   = {1},

  Abstract                 = {A heuristic algorithm for multiple vehicles routing problem with stochastic demand is proposed and the goal is to minimize the total traveling cost. Two-phase method is adopted to deal with this problem. In the first phase, an algorithm is proposed to partition customers into clusters, and the main task of the second phase is to design an effective routing through each cluster of customers to minimize the total expected traveling cost. Both the a priori strategy and the reoptimization strategy are used to obtain the optimal routing. The experiment results indicate that this method can produce solutions of good quality and is an effective algorithm for the multiple vehicles routing problem with stochastic demand},
  Doi                      = {{10.1109/WCICA.2006.1712640}},
  Keywords                 = {a priori strategy, heuristic algorithm, multiple vehicles routing, optimal routing, optimisation, reoptimization, reoptimization strategy, stochastic demand, stochastic processes, stochastic vehicle routing problem, transportation, traveling cost, vehicles, {VRPSD}},
  Owner                    = {Andres},
  Timestamp                = {2009.11.19}
}

@Article{Gans_1999,
  Title                    = {Dynamic Vehicle Dispatching: Optimal Heavy Traffic Performance and Practical Insights},
  Author                   = {Noah Gans and Garrett van Ryzin},
  Journal                  = {Operations Research},
  Year                     = {1999},
  Number                   = {5},
  Pages                    = {pp. 675--692},
  Volume                   = {47},

  Abstract                 = {We analyze a general model of dynamic vehicle dispatching systems in which congestion is the primary measure of performance. In the model, a finite collection of tours are dynamically dispatched to deliver loads that arrive randomly over time. A load waits in queue until it is assigned to a tour. This representation, which is analogous to classical set-covering models, can be used to study a variety of dynamic routing and load consolidation problems. We characterize the optimal work in the system in heavy traffic using a lower bound from our earlier work (Gans and van Ryzin 1997) and an upper bound which is based on a simple batching policy. These results give considerable insight into how various parameters of the problem affect system congestion. In addition, our analysis suggests a practical heuristic which, in simulation experiments, significantly outperforms more conventional dispatching policies. The heuristic uses a few simple principles to control congestion, principles which can be easily incorporated within classical, static routing algorithms.},
  Copyright                = {Copyright © 1999 INFORMS},
  ISSN                     = {0030364X},
  Jstor_articletype        = {research-article},
  Jstor_formatteddate      = {Sep. - Oct., 1999},
  Language                 = {English},
  Publisher                = {INFORMS},
  Url                      = {http://www.jstor.org/stable/223092}
}

@Article{gendreau_exact_1995,
  Title                    = {An Exact Algorithm for the Vehicle Routing Problem with Stochastic Demands and Customers},
  Author                   = {Michel Gendreau and Gilbert Laporte and Rene Seguin},
  Journal                  = {TRANSPORTATION SCIENCE},
  Year                     = {1995},

  Month                    = may,
  Number                   = {2},
  Pages                    = {143--155},
  Volume                   = {29},

  Abstract                 = {In this article, the following stochastic vehicle routing problem is considered. Each customer has a known probability of presence and a random demand. This problem arises in several contexts, e.g., in the design of less-than-truckload collection routes. Because of uncertainty, it may not be possible to follow vehicle routes as planned. Using a stochastic programming framework, the problem is solved in two stages. In a first stage, planned collection routes are designed. In a second stage, when the set of present customers is known, these routes are followed as planned by skipping the absent customers. Whenever the vehicle capacity is attained or exceeded, the vehicle returns to the depot and resumes its collections along the planned route. This generates a penalty. The problem is to design a first stage solution in order to minimize the expected total cost of the second state solution. This is formulated as a stochastic integer program, and solved for the first time to optimality by means of an integer L-shaped method.},
  Doi                      = {10.1287/trsc.29.2.143},
  File                     = {HighWire Snapshot:/home/andres/.mozilla/firefox/q5r35cg1.default/zotero/storage/UQH67ZIA/143.html:text/html},
  Url                      = {http://transci.journal.informs.org/cgi/content/abstract/29/2/143}
}

@Article{gendreau_stochastic_1996,
  Title                    = {Stochastic vehicle routing},
  Author                   = {Michel Gendreau and Gilbert Laporte and Ren Sguin},
  Journal                  = {European Journal of Operational Research},
  Year                     = {1996},
  Number                   = {1},
  Pages                    = {3--12},
  Volume                   = {88},

  Doi                      = {{10.1016/0377-2217(95)00050-X}},
  Owner                    = {Andres},
  Timestamp                = {2009.11.18},
  Url                      = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VCT-3VWPNM7-M&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1060092995&_rerunOrigin=scholar.google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=bedf5fccb4e6613b5daeec589f15d9ce}
}

@Article{Goodson2013,
  Title                    = {{Rollout policies for dynamic solutions to the multivehicle routing problem with stochastic demand and duration limits}},
  Author                   = {Goodson, JC},
  Journal                  = {Operations Research},
  Year                     = {2013},
  Number                   = {1},
  Pages                    = {pp. 138--154},
  Volume                   = {61},

  Abstract                 = {We develop a family of rollout policies based on fixed routes to obtain dynamic solutions to the vehicle routing problem with stochastic demand and duration limits (VRPSDL). In addition to a traditional one-step rollout policy, we leverage the notions of the pre- and post-decision state to distinguish two additional rollout variants. We tailor our rollout policies by developing a dynamic decomposition scheme that achieves high quality solutions to large problem instances with reasonable computational effort. Computational experiments demonstrate that our rollout policies improve upon the performance of a rolling horizon procedure and commonly employed fixed-route policies, with improvement over the latter being more substantial.},
  Keywords                 = {rollout,vrpsd},
  Mendeley-tags            = {rollout,vrpsd},
  Owner                    = {ajaque},
  Timestamp                = {2015.01.29},
  Url                      = {http://pubsonline.informs.org/doi/abs/10.1287/opre.1120.1127}
}

@PhdThesis{Goodson2010,
  Title                    = {Solution methodologies for vehicle routing problems with stochastic demand},
  Author                   = {Goodson, JC},
  Year                     = {2010},

  Annote                   = {Christiansen and Lysgaard (2007) find optimal solution to different VRPSD with multiple vehicles.},
  Keywords                 = {rollout,vrpsd},
  Mendeley-tags            = {rollout,vrpsd},
  Owner                    = {ajaque},
  Timestamp                = {2015.01.29},
  Url                      = {http://ir.uiowa.edu/etd/675/}
}

@Article{haghani_dynamic_2005,
  Title                    = {A dynamic vehicle routing problem with time-dependent travel times},
  Author                   = {Ali Haghani and Soojung Jung},
  Journal                  = {Computers \& Operations Research},
  Year                     = {2005},

  Month                    = nov,
  Number                   = {11},
  Pages                    = {2959--2986},
  Volume                   = {32},

  Abstract                 = {In this paper we present a formulation for the dynamic vehicle routing problem with time-dependent travel times. We also present a genetic algorithm to solve the problem. The problem is a pick-up or delivery vehicle routing problem with soft time windows in which we consider multiple vehicles with different capacities, real-time service requests, and real-time variations in travel times between demand nodes. The performance of the genetic algorithm is evaluated by comparing its results with exact solutions and lower bounds for randomly generated test problems. For small size problems with up to 10 demands, the genetic algorithm provides almost the same results as the exact solutions, while its computation time is less than 10\% of the time required to produce the exact solutions. For the problems with 30 demand nodes, the genetic algorithm results have less than 8\% gap with lower bounds. This research also shows that as the uncertainty in the travel time information increases, a dynamic routing strategy that takes the real-time traffic information into account becomes increasingly superior to a static one. This is clear when we compare the static and dynamic routing strategies in problem scenarios that have different levels of uncertainty in travel time information. In additional tests on a simulated network, the proposed algorithm works well in dealing with situations in which accidents cause significant congestion in some part of the transportation network.},
  Doi                      = {10.1016/j.cor.2004.04.013},
  ISSN                     = {0305-0548},
  Keywords                 = {Genetic algorithm, Network, Optimization, Time dependent, Travel time, Vehicle routing},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B6VC5-4CS4J13-2/2/6f96cb8477764df992523cb7ed91de7f}
}

@Book{Holland1975,
  Title                    = {Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence},
  Author                   = {Holland, John Henry},
  Publisher                = {University of Michigan Press},
  Year                     = {1975},

  ISBN                     = {0472084607},
  Keywords                 = {ga},
  Mendeley-tags            = {ga},
  Owner                    = {ajaque},
  Pages                    = {183},
  Timestamp                = {2015.01.29},
  Url                      = {http://books.google.com.co/books/about/Adaptation\_in\_natural\_and\_artificial\_sys.html?id=JE5RAAAAMAAJ\&pgis=1}
}

@InProceedings{Markovic_2005,
  Title                    = {Using data mining to forecast uncertain demands in stochastic vehicle routing problem},
  Author                   = {Ivana Cavar Ton?i Cari? {Hrvoje Markovic}},
  Booktitle                = {13th International Symposium on Elecronics in Transport (ISEP), Slovenia},
  Year                     = {2005},

  Owner                    = {Andres},
  Timestamp                = {2009.11.19}
}

@Book{Gerd_sp_state_2011,
  Title                    = {Stochastic Programming: The State of the Art in Honor of George B. Dantzig},
  Author                   = {Gerd Infanger and George B. Dantzig},
  Editor                   = {Gerd Infanger},
  Publisher                = {Springer, International Series in Operations Research and Management Science},
  Year                     = {2011},

  Owner                    = {andres},
  Timestamp                = {2012.02.27}
}

@Article{jothi_approximatingk-traveling_2007,
  Title                    = {Approximating the k-traveling repairman problem with repairtimes},
  Author                   = {Raja Jothi and Balaji Raghavachari},
  Journal                  = {Journal of Discrete Algorithms},
  Year                     = {2007},

  Month                    = jun,
  Number                   = {2},
  Pages                    = {293--303},
  Volume                   = {5},

  Abstract                 = {Given an undirected graph {G=(V,E)} and a source vertex s[set membership, {variant]V,} the k-traveling repairman {(KTR)} problem, also known as the minimum latency problem, asks for k tours, each starting at s and together covering all the vertices (customers) such that the sum of the latencies experienced by the customers is minimum. The latency of a customer p is defined to be the distance traveled (time elapsed) before visiting p for the first time. Previous literature on the {KTR} problem has considered the version of the problem in which the repairtime of a customer is assumed to be zero for latency calculations. We consider a generalization of the problem in which each customer has an associated repairtime. For a fixed k, we present a ([beta]+2)-approximation algorithm for this problem, where [beta] is the best achievable approximation ratio for the {KTR} problem with zero repairtimes (currently [beta]=6). For arbitrary k, we obtain a -approximation ratio. When the repairtimes of the customers are all the same, we present an approximation algorithm with a better ratio.2 We also introduce the bounded-latency problem, a complementary version of the {KTR} problem, in which we are given a latency bound L and are asked to find the minimum number of repairmen required to service all the customers such that the latency of no customer is more than L. For this problem, we present a simple bicriteria approximation algorithm that finds a solution with at most 2/[rho] times the number of repairmen required by an optimal solution, with the latency of no customer exceeding {(1+[rho])L,} [rho]{\textgreater}0.},
  Doi                      = {10.1016/j.jda.2006.03.023},
  ISSN                     = {1570-8667},
  Keywords                 = {Approximation algorithms, Combinatorial optimization},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B758J-4KGG1TF-1/2/3653dbb5b223af051c60949d92893e5d}
}

@Article{laporte_integer_2002,
  Title                    = {An Integer L-Shaped Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Demands},
  Author                   = {Gilbert Laporte and Fran\c{C}ois V. Louveaux and Luc {Van Hamme}},
  Journal                  = {Operations Research},
  Year                     = {2002},

  Month                    = may,
  Number                   = {3},
  Pages                    = {415--423},
  Volume                   = {50},

  Abstract                 = {The classical Vehicle Routing Problem consists ofdetermining optimal routes for m identical vehicles, starting and leaving at the depot, such that every customer is visited exactly once. In the capacitated version {(CVRP)} the total demand collected along a route cannot exceed the vehicle capacity. This article considers the situation where some ofthe demands are stochastic. This implies that the level of demand at each customer is not known before arriving at the customer. In some cases, the vehicle may thus be unable to load the customer's demand, even ifthe expected demand along the route does not exceed the vehicle capacity. Such a situation is referred to as a failure. The capacitated vehicle routing problem with stochastic demands {(SVRP)} then consists ofminimizing the total cost ofthe planned routes and of expected failures. Here, penalties for failures correspond to return trips to the depot. The vehicle first returns to the depot to unload, then resumes its trip as originally planned. This article studies an implementation of the Integer L-shaped method for the exact solution of the {SVRP.} It develops new lower bounds on the expected penalty for failures. In addition, it provides variants of the optimality cuts for the {SVRP} that also hold at fractional solutions. Numerical experiments indicate that some instances involving up to 100 customers and few vehicles can be solved to optimality within a relatively short computing time.},
  Doi                      = {10.1287/opre.50.3.415.7751},
  File                     = {Snapshot:/home/andres/.mozilla/firefox/q5r35cg1.default/zotero/storage/5GTKRQTF/415.html:text/html},
  ISSN                     = {{0030-364X}, 1526-5463},
  Keywords                 = {Programming: stochastic, Transportation: stochastic vehicle routing},
  Url                      = {http://or.journal.informs.org/content/50/3/415}
}

@Article{Lenstra1981,
  Title                    = {Complexity of vehicle routing and scheduling problems},
  Author                   = {Lenstra, J. K. and Kan, A. H. G. Rinnooy},
  Journal                  = {Networks},
  Year                     = {1981},

  Month                    = jan,
  Number                   = {2},
  Pages                    = {221--227},
  Volume                   = {11},

  Annote                   = {This paper shows that VRP is NP-Hard},
  Doi                      = {10.1002/net.3230110211},
  ISSN                     = {00283045},
  Keywords                 = {vrp},
  Mendeley-tags            = {vrp},
  Owner                    = {ajaque},
  Timestamp                = {2015.01.29},
  Url                      = {http://doi.wiley.com/10.1002/net.3230110211}
}

@Article{mendoza_memetic_2010,
  Title                    = {A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands},
  Author                   = {Jorge E. Mendoza and Bruno Castanier and Christelle Gu{\'e}ret and Andr{\'e}s L. Medaglia and Nubia Velasco},
  Journal                  = {Computers \& Operations Research},
  Year                     = {2010},

  Month                    = nov,
  Number                   = {11},
  Pages                    = {1886--1898},
  Volume                   = {37},

  Abstract                 = {The multi-compartment vehicle routing problem {(MC-VRP)} consists of designing transportation routes to satisfy the demands of a set of customers for several products that, because of incompatibility constraints, must be loaded in independent vehicle compartments. Despite its wide practical applicability the {MC-VRP} has not received much attention in the literature, and the few existing methods assume perfect knowledge of the customer demands, regardless of their stochastic nature. This paper extends the {MC-VRP} by introducing uncertainty on what it is known as the {MC-VRP} with stochastic demands {(MC-VRPSD).} The {MC-VRPSD} is modeled as a stochastic program with recourse and solved by means of a memetic algorithm. The proposed memetic algorithm couples genetic operators and local search procedures proven to be effective on deterministic routing problems with a novel individual evaluation and reparation strategy that accounts for the stochastic nature of the problem. The algorithm was tested on instances of up to 484 customers, and its results were compared to those obtained by a savings-based heuristic and a memetic algorithm {(MA/SCS)} for the {MC-VRP} that uses a spare capacity strategy to handle demand fluctuations. In addition to effectively solve the {MC-VRPSD}, the proposed {MA/SCS} also improved 14 best known solutions in a 40-problem testbed for the {MC-VRP.}},
  Doi                      = {10.1016/j.cor.2009.06.015},
  File                     = {ScienceDirect Full Text PDF:/home/andres/.mozilla/firefox/q5r35cg1.default/zotero/storage/W9KF6FTP/Mendoza et al. - 2010 - A memetic algorithm for the multi-compartment vehi.pdf:application/pdf;ScienceDirect Snapshot:/home/andres/.mozilla/firefox/q5r35cg1.default/zotero/storage/6WF8AXDD/S0305054809001713.html:text/html},
  ISSN                     = {0305-0548},
  Keywords                 = {Evolutionary algorithms, Memetic algorithms, Multi-compartment vehicle routing problem, stochastic demands},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0305054809001713}
}

@Article{Moretti,
  Title                    = {Adaptive granular local search heuristic for a dynamic vehicle routing problem},
  Author                   = {Rodrigo Moretti},
  Journal                  = {Computers \& Operations Research},
  Year                     = {2009},
  Pages                    = {2955--2968},
  Volume                   = {36},

  Owner                    = {emily},
  Timestamp                = {2009.10.18}
}

@Article{novoa_approximate_2009,
  Title                    = {An approximate dynamic programming approach for the vehicle routing problem with stochastic demands},
  Author                   = {Clara Novoa and Robert Storer},
  Journal                  = {European Journal of Operational Research},
  Year                     = {2009},

  Month                    = jul,
  Number                   = {2},
  Pages                    = {509--515},
  Volume                   = {196},

  Abstract                 = {This paper examines approximate dynamic programming algorithms for the single-vehicle routing problem with stochastic demands from a dynamic or reoptimization perspective. The methods extend the rollout algorithm by implementing different base sequences (i.e. a priori solutions), look-ahead policies, and pruning schemes. The paper also considers computing the cost-to-go with Monte Carlo simulation in addition to direct approaches. The best new method found is a two-step lookahead rollout started with a stochastic base sequence. The routing cost is about 4.8\% less than the one-step rollout algorithm started with a deterministic sequence. Results also show that Monte Carlo cost-to-go estimation reduces computation time 65\% in large instances with little or no loss in solution quality. Moreover, the paper compares results to the perfect information case from solving exact a posteriori solutions for sampled vehicle routing problems. The confidence interval for the overall mean difference is (3.56\%, 4.11\%).},
  Doi                      = {10.1016/j.ejor.2008.03.023},
  ISSN                     = {0377-2217},
  Keywords                 = {Approximate dynamic programming, Stochastic vehicle routing, Transportation},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B6VCT-4S4JYPK-1/2/e02ef985fd85d60bd61909d8029ea963}
}

@Article{parragh_surveypickup_2008,
  Title                    = {A survey on pickup and delivery problems},
  Author                   = {Sophie Parragh and Karl Doerner and Richard Hartl},
  Journal                  = {Journal f{\~A}{\OE}r Betriebswirtschaft},
  Year                     = {2008},

  Month                    = apr,
  Number                   = {1},
  Pages                    = {21--51},
  Volume                   = {58},

  Abstract                 = {Abstract This paper is the first part of a comprehensive survey on pickup and delivery problems. Basically, two problem classes can be distinguished. The first class, discussed in this paper, deals with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. This class is denoted as Vehicle Routing Problems with Backhauls {(VRPB).} Four subtypes can be considered, namely the Vehicle Routing Problem with Clustered Backhauls {(VRPCB} {\^a} all linehauls before backhauls), the Vehicle Routing Problem with Mixed linehauls and Backhauls {(VRPMB} {\^a} any sequence of linehauls and backhauls permitted), the Vehicle Routing Problem with Divisible Delivery and Pickup {(VRPDDP} {\^a} customers demanding delivery and pickup service can be visited twice), and the Vehicle Routing Problem with Simultaneous Delivery and Pickup {(VRPSDP} {\^a} customers demanding both services have to be visited exactly once). The second class, dealt with in the second part of this survey, refers to all those problems where goods are transported between pickup and delivery locations. These are the Pickup and Delivery Vehicle Routing Problem {(PDVRP} {\^a} unpaired pickup and delivery points), the classical Pickup and Delivery Problem {(PDP} {\^a} paired pickup and delivery points), and the {Dial-A-Ride} Problem {(DARP} {\^a} passenger transportation between paired pickup and delivery points and user inconvenience taken into consideration). Single as well as multi vehicle versions of the mathematical problem formulations are given for all four {VRPB} types, the corresponding exact, heuristic, and metaheuristic solution methods are discussed.},
  Doi                      = {10.1007/s11301-008-0033-7},
  Owner                    = {Andres Jaque},
  Timestamp                = {2009.05.10},
  Url                      = {http://dx.doi.org/10.1007/s11301-008-0033-7}
}

@Article{repoussis_web-based_????,
  Title                    = {A web-based decision support system for waste lube oils collection and recycling},
  Author                   = {P.P. Repoussis and D.C. Paraskevopoulos and G. Zobolas and C.D. Tarantilis and G. Ioannou},
  Journal                  = {European Journal of Operational Research},

  Abstract                 = {This paper presents a web-based decision support system (DSS) that enables schedulers to tackle reverse supply chain management problems interactively. The focus is on the efficient and effective management of waste lube oils collection and recycling operations. The emphasis is given on the systemic dimensions and modular architecture of the proposed DSS. The latter incorporates intra- and inter-city vehicle routing with real-life operational constraints using shortest path and sophisticated hybrid metaheuristic algorithms. It is also integrated with an Enterprise Resource Planning system allowing the utilization of particular functional modules and the combination with other peripheral planning tools. Furthermore, the proposed DSS provides a framework for on-line monitoring and reporting to all stages of the waste collection processes. The system is developed using a web architecture that enables sharing of information and algorithms among multiple sites, along with wireless telecommunication facilities. The application to an industrial environment showed improved productivity and competitiveness, indicating its applicability on realistic reverse logistical planning problems. Keywords: Decision support systems; Vehicle routing; Waste management },
  Doi                      = {10.1016/j.ejor.2007.11.004},
  Keywords                 = {Decision support systems, Vehicle routing, Waste management},
  Owner                    = {Andres Jaque},
  Timestamp                = {2009.05.03},
  Url                      = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VCT-4R3C04G-2&_user=10&_coverDate=11%2F09%2F2007&_alid=803992512&_rdoc=88&_fmt=high&_orig=search&_cdi=5963&_sort=d&_docanchor=&view=c&_ct=543&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=3ac92ff9efc19d48e17297980aaf73de}
}

@Book{ruszczynski_handbooks_2003,
  Title                    = {Handbooks in Operations Research and Management Science},
  Author                   = {Ruszczynski},
  Publisher                = {Elsevier},
  Year                     = {2003},
  Month                    = jun,

  ISBN                     = {0444508546, 9780444508546},
  Owner                    = {andres},
  Pages                    = {700},
  Timestamp                = {2010.04.20}
}

@Article{secomandi_rollout_2001,
  Title                    = {A rollout policy for the vehicle routing problem with stochastic demands},
  Author                   = {N. Secomandi},
  Journal                  = {Operations Research},
  Year                     = {2001},
  Pages                    = {796--802}
}

@Article{secomandi_comparing_2000,
  Title                    = {Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands},
  Author                   = {Nicola Secomandi},
  Journal                  = {Computers \& Operations Research},
  Year                     = {2000},

  Month                    = sep,
  Number                   = {11-12},
  Pages                    = {1201--1225},
  Volume                   = {27},

  Abstract                 = {The paper considers a version of the vehicle routing problem where customers' demands are uncertain. The focus is on dynamically routing a single vehicle to serve the demands of a known set of geographically dispersed customers during real-time operations. The goal consists of minimizing the expected distance traveled in order to serve all customers' demands. Since actual demand is revealed upon arrival of the vehicle at the location of each customer, fully exploiting this feature requires a dynamic approach. This work studies the suitability of the emerging field of neuro-dynamic programming {(NDP)} in providing approximate solutions to this difficult stochastic combinatorial optimization problem. The paper compares the performance of two {NDP} algorithms: optimistic approximate policy iteration and a rollout policy. While the former improves the performance of a nearest-neighbor policy by 2.3\%, the computational results indicate that the rollout policy generates higher quality solutions. The implication for the practitioner is that the rollout policy is a promising candidate for vehicle routing applications where a dynamic approach is {required.Scope} and purpose Recent years have seen a growing interest in the development of vehicle routing algorithms to cope with the uncertain and dynamic situations found in real-world applications (see the recent survey paper by Powell et al. [1]). As noted by Psaraftis [2], dramatic advances in information and communication technologies provide new possibilities and opportunities for vehicle routing research and applications. The enhanced capability of capturing the information that becomes available during real-time operations opens up new research directions. This informational availability provides the possibility of developing dynamic routing algorithms that take advantage of the information that is dynamically revealed during operations. Exploiting such information presents a significant challenge to the operations research/management science community. The single vehicle routing problem with stochastic demands [3] provides an example of a simple, yet very difficult to solve exactly, dynamic vehicle routing problem [2, p. 157]. The problem can be formulated as a stochastic shortest path problem [4] characterized by an enormous number of states. Neuro-dynamic programming [5 and 6] is a recent methodology that can be used to approximately solve very large and complex stochastic decision and control problems. In this spirit, this paper is meant to study the applicability of neuro-dynamic programming algorithms to the single-vehicle routing problem with stochastic demands.},
  Doi                      = {{10.1016/S0305-0548(99)00146-X}},
  ISSN                     = {0305-0548},
  Keywords                 = {Heuristics, Neuro-dynamic programming, Rollout policies, Stochastic vehicle routing},
  Owner                    = {Andres},
  Timestamp                = {2009.11.18},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B6VC5-40797G7-C/2/47fa588b8d2fa6587b568f7a6ec7ff4c}
}

@PhdThesis{Secomandi_1998,
  Title                    = {Exact and Heuristic Dynamic Programming Algorithms for the Vehicle Routing Problem with Stochastic Demands},
  Author                   = {Nicola Secomandi},
  School                   = {Deparment of Decision and Information Sciences, University of Houston},
  Year                     = {1998},

  Address                  = {Houston, TX}
}

@Article{slater_specification_2002,
  Title                    = {Specification for a dynamic vehicle routing and scheduling system},
  Author                   = {Alan Slater},
  Journal                  = {International Journal of Transport Management},
  Year                     = {2002},

  Month                    = feb,
  Number                   = {1},
  Pages                    = {29--40},
  Volume                   = {1},

  Doi                      = {{10.1016/S1471-4051(01)00004-0}},
  ISSN                     = {1471-4051},
  Keywords                 = {{GPS} tracking and tracing, Heuristics, Parallel insertion algorithms, Parallel tour-building Algorithms, Time Windows, Transport planning, Vehicle routing and scheduling},
  Owner                    = {andres},
  Timestamp                = {2009.11.21},
  Url                      = {http://www.bases.unal.edu.co:2053/science/article/B6W83-458P7KM-3/2/d13c03217ac8d1243fea00ac92701be0}
}

@Article{Tapas,
  Title                    = {Analysis of asymmetric patrolling repairman systems},
  Author                   = {Martin A. Wortman {Tapas K. Das}},
  Journal                  = {European Journal of Operational Research},
  Year                     = {1993},
  Pages                    = {45--60},
  Volume                   = {64},

  Owner                    = {emily},
  Timestamp                = {2009.10.18}
}

@Article{Timon,
  Title                    = {Dynamic vehicle routing for online B2C delivery},
  Author                   = {Eldon Y. Li {Timon C. Du} and Defrose Chouc},
  Journal                  = {The international journal of management science},
  Year                     = {2004},
  Pages                    = {33--45},
  Volume                   = {33},

  Owner                    = {emily},
  Timestamp                = {2009.10.18}
}

@Book{toth_vehicle_2001,
  Title                    = {The vehicle routing problem},
  Author                   = {Paolo Toth and Daniele Vigo},
  Publisher                = {{SIAM}},
  Year                     = {2001},

  ISBN                     = {0898715792, 9780898715798},
  Owner                    = {Andres Jaque},
  Pages                    = {385},
  Timestamp                = {2009.07.30}
}

@Book{toth_vehicle_1987,
  Title                    = {The vehicle routing problem},
  Author                   = {P. Toth and D. Vigo},
  Publisher                = {Society for Industrial Mathematics},
  Year                     = {1987},

  Owner                    = {Andres},
  Timestamp                = {2009.10.18}
}

@Article{yang_stochastic_2000,
  Title                    = {Stochastic Vehicle Routing Problem with Restocking},
  Author                   = {{Wen-Huei} Yang and Kamlesh Mathur and Ronald H. Ballou},
  Journal                  = {{TRANSPORTATION} {SCIENCE}},
  Year                     = {2000},

  Month                    = feb,
  Number                   = {1},
  Pages                    = {99--112},
  Volume                   = {34},

  Abstract                 = {In this paper, a stochastic vehicle routing problem is considered. In particular, customer demand is assumed to be uncertain, and actual demand is revealed only upon the visit to the customer. Instead of adopting the simple recourse action of returning to the depot whenever the vehicle runs out of stock, the points along the route at which restocking is to occur are designed into the route. The restocking points may be before a stockout actually occurs. Two heuristic algorithms are developed to construct both single and multiple routes that minimize total travel cost. The computational results show that the heuristic procedures produce quality solutions and are efficient.},
  Doi                      = {10.1287/trsc.34.1.99.12278},
  Owner                    = {Andres},
  Timestamp                = {2009.11.18},
  Url                      = {http://transci.journal.informs.org/cgi/content/abstract/34/1/99}
}

